# Automated Structure Discovery in Atomic Force Microscopy

## TL;DR

This paper introduces a deep learning method that interprets AFM images to determine the atomic structure of molecules, enabling analysis of complex, non-planar molecules previously difficult to resolve.

## Contribution

The authors develop a novel deep learning framework that directly predicts molecular structures from AFM images, expanding high-resolution AFM applications to diverse molecular systems.

## Key findings

- Successfully resolved multiple adsorption configurations of 1S-camphor on Cu(111)
- Demonstrated the ability to interpret distorted AFM images of non-planar molecules
- Enabled direct prediction of molecular structures from AFM data

## Abstract

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules, due to difficulties with interpretation of highly distorted AFM images originating from non-planar molecules. Here we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to apply high-resolution AFM to a large variety of systems for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.